Fraud Detection: A Hybrid Approach With Logistic Regression, Decision Tree, and Random Forest
Yugal Salunke, Saroj Sandeep Phalke, Manoj Madavi, Praali Kumre, Grishma Bobhate · Cureus Journal of Computer Science. · 2025
The rise of financial fraud, not only in India but also around the world, is a major problem. Credit card transactions have been steadily increasing in recent years, as have internet payments like Unified Payments Interface and debit card transactions, which have been increasing every day. As a result, fraud is increasing, and it has become easier for fraudsters to commit fraud. Recent research indicates the practicality of machine learning algorithms for recognizing payment fraud. Since credit cards are a common target, online fraudsters simply commit fraud because of excess use of e-commerce and other platforms, which has led to an increase in online payment methods, resulting in a greater risk of online fraud. Many researchers have started using machine learning algorithms to detect fraud. The primary objective of this study is to create a better fraud detection algorithm by analyzing payment transactional data, focusing on customers' transactional data and their payment behavioral patterns. The study proposes a framework that groups cardholders based on the volume of their transactions. Financial fraud is the biggest threat to world economies in terms of financial losses each year. The task of accurately and efficiently detecting fraud is, therefore, very challenging due to the high volume of transaction data and its complex nature. This paper presents a hybrid machine learning approach using logistic regression, decision trees, and random forests that could help enhance the accuracy and reliability of fraud detection systems. The study used an available credit card fraud dataset, with a focus on feature engineering and model evaluation to compare individual algorithms versus their ensemble. Experimental results show that the ensemble outperformed the individual classifiers in terms of precision, recall, and overall accuracy. This paper underlines the potential of hybrid machine learning techniques in improving fraud detection systems and proposes avenues for further research.